Main network model diagnostics - unbalanced statistics

This file shows diagnostics for main network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. Here we try to identify which term added to a model with nodefactor(race) and nodematch(race) creates issues with MCMC diagnostics for race matching. This is motivated by observing, in the “dx_main_unbalanced_buildup.Rmd” file that the MCMC diagnostics for race matching are off when added to a model with nodefactor(race), nodefactor(region), and nodefactor(deg.pers). We will see if we can get it to fit in the absence of these other nodefactor terms and then see how the fit changes with the addition of each alone.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")

load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.m.testracemix.unbal.rda")

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4
edges 2240.5 2240.5 2240.5 2240.5
nodefactor.deg.pers.1 NA 474.0 NA 474.0
nodefactor.deg.pers.2 NA 605.0 NA 605.0
nodefactor.race..wa.B 208.0 208.0 208.0 208.0
nodefactor.race..wa.H 535.0 535.0 535.0 535.0
nodefactor.region.EW NA NA 445.6 445.6
nodefactor.region.OW NA NA 1278.1 1278.1
nodematch.race..wa.B 31.2 31.2 31.2 31.2
nodematch.race..wa.H 123.3 123.3 123.3 123.3
nodematch.race..wa.O 1638.9 1638.9 1638.9 1638.9
degrange 0.0 0.0 0.0 0.0
nodematch.role.class.I -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = control.ergm(MCMC.interval = 1e+5,
                                MCMC.samplesize = 7500,
                                MCMC.burnin = 1e+6,
                                MPLE.max.dyad.types = 1e+7,
                                init.method = "zeros",
                                MCMLE.maxit = 400,
                                parallel = np/2, 
                                parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -0.5888 29.090  0.16795        0.16760
## nodefactor.race..wa.B  2.2717 12.615  0.07283        0.07374
## nodefactor.race..wa.H  3.6319 17.782  0.10267        0.10820
## nodematch.race..wa.B  -2.4341  4.881  0.02818        0.02958
## nodematch.race..wa.H  -2.0348  8.758  0.05057        0.05943
## nodematch.race..wa.O   2.1653 26.548  0.15328        0.15360
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%    50%     75%  97.5%
## edges                 -57.50 -20.500 -0.500 18.5000 56.500
## nodefactor.race..wa.B -22.00  -5.997  2.003 11.0032 27.003
## nodefactor.race..wa.H -30.98  -7.978  4.022 16.0220 39.022
## nodematch.race..wa.B  -11.18  -6.179 -2.179  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.312  3.6876 15.688
## nodematch.race..wa.O  -49.89 -15.890  2.110 20.1103 54.110
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.race..wa.B
## edges                 1.00000000          0.2620776764
## nodefactor.race..wa.B 0.26207768          1.0000000000
## nodefactor.race..wa.H 0.34743587         -0.0070735216
## nodematch.race..wa.B  0.09536128          0.5579924897
## nodematch.race..wa.H  0.15568397          0.0004510425
## nodematch.race..wa.O  0.80737391         -0.0805027114
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.347435872          0.095361283
## nodefactor.race..wa.B          -0.007073522          0.557992490
## nodefactor.race..wa.H           1.000000000         -0.004504263
## nodematch.race..wa.B           -0.004504263          1.000000000
## nodematch.race..wa.H            0.642445047         -0.006654524
## nodematch.race..wa.O           -0.074636377          0.024051407
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                         0.1556839733           0.80737391
## nodefactor.race..wa.B         0.0004510425          -0.08050271
## nodefactor.race..wa.H         0.6424450469          -0.07463638
## nodematch.race..wa.B         -0.0066545236           0.02405141
## nodematch.race..wa.H          1.0000000000           0.06873998
## nodematch.race..wa.O          0.0687399776           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.004395513           0.020419275           0.060859744
## Lag 2e+05 -0.026191457           0.008962724           0.011118240
## Lag 3e+05 -0.013976462           0.014586038           0.005848958
## Lag 4e+05  0.023216152          -0.025990394          -0.004107018
## Lag 5e+05  0.031064558          -0.010150850           0.019695546
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.066105480          0.161483610          0.010185769
## Lag 2e+05          0.021644962          0.033158792         -0.008212128
## Lag 3e+05          0.001045170         -0.008162836         -0.002631588
## Lag 4e+05         -0.004031994         -0.003182427          0.001126897
## Lag 5e+05         -0.011051796          0.026007860         -0.012187514
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.003397064           0.054976439           0.026977348
## Lag 2e+05 -0.019824387           0.015191315           0.003613058
## Lag 3e+05  0.013613367          -0.005098036           0.013292039
## Lag 4e+05 -0.015340242           0.001589431          -0.013265100
## Lag 5e+05  0.006313155           0.017459128          -0.009721128
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.030387541          0.151467123         -0.006905525
## Lag 2e+05         -0.007348548          0.013167747         -0.011246920
## Lag 3e+05         -0.006935945         -0.005524852          0.006485847
## Lag 4e+05         -0.012635452         -0.014149893         -0.008860804
## Lag 5e+05          0.003289638         -0.028582867          0.005429486
## Chain 3 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0166249912           0.013802121           0.059533648
## Lag 2e+05 -0.0168914583           0.003975351           0.007103057
## Lag 3e+05 -0.0083286402          -0.031094416           0.002127982
## Lag 4e+05  0.0233799893          -0.020900812          -0.009523470
## Lag 5e+05 -0.0008329317           0.014320530          -0.015748336
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.054563286          0.172602838          0.001849392
## Lag 2e+05          0.021204629          0.027586768          0.003891533
## Lag 3e+05         -0.038873026         -0.006653445         -0.008710939
## Lag 4e+05         -0.020848296         -0.007238560          0.014145819
## Lag 5e+05         -0.007284489         -0.016159381         -0.004217299
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.002252247          -0.008296957           0.075434941
## Lag 2e+05  0.012914053          -0.006995296          -0.017109464
## Lag 3e+05  0.023383618           0.008620317           0.006418072
## Lag 4e+05 -0.011699866          -0.035683585           0.021846200
## Lag 5e+05  0.043066601           0.007206307          -0.012936708
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.04084675          0.166542942         -0.009338227
## Lag 2e+05           0.01134967         -0.007220387          0.002630321
## Lag 3e+05          -0.02397941         -0.004189423          0.008611681
## Lag 4e+05          -0.02469803          0.024831086         -0.009281773
## Lag 5e+05          -0.01672396         -0.009808596          0.038836696
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.003338815          -0.003062388           0.053921333
## Lag 2e+05 -0.002243389           0.017845937           0.015116882
## Lag 3e+05  0.010767833          -0.007683173          -0.002680704
## Lag 4e+05  0.000516932          -0.007580719          -0.021479767
## Lag 5e+05 -0.004003865          -0.015759195           0.007424889
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.048077229          0.156754289         -0.006880084
## Lag 2e+05          0.029449368          0.029172676         -0.010402550
## Lag 3e+05          0.019784050          0.002776108         -0.002020655
## Lag 4e+05          0.006373965         -0.004178073          0.011031636
## Lag 5e+05         -0.001101402          0.014639249         -0.006909848
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000            1.00000000
## Lag 1e+05  0.028297311          0.0415389514            0.04650799
## Lag 2e+05 -0.006908908         -0.0089962284            0.01393627
## Lag 3e+05 -0.038042938          0.0008218908            0.01464797
## Lag 4e+05  0.014542226         -0.0201398443           -0.01606172
## Lag 5e+05  0.003590850         -0.0113321288           -0.01108692
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.041175504         0.1733519157          0.014900510
## Lag 2e+05          0.032830697        -0.0108903813         -0.015348452
## Lag 3e+05         -0.002298667        -0.0208981511         -0.003092752
## Lag 4e+05         -0.004047677        -0.0102521208          0.037025537
## Lag 5e+05         -0.025709261         0.0000272084          0.004692202
## Chain 7 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000e+00           1.000000000           1.000000000
## Lag 1e+05  1.744591e-02           0.004978197           0.060463659
## Lag 2e+05  8.299807e-03          -0.011936628          -0.015451127
## Lag 3e+05 -3.255698e-02          -0.019543052          -0.004381679
## Lag 4e+05 -4.193396e-05           0.005997974          -0.020359970
## Lag 5e+05  1.864253e-02          -0.022160024          -0.002338753
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000         1.0000000000
## Lag 1e+05          0.071962489           0.18865793         0.0189374020
## Lag 2e+05         -0.005048535           0.02244226         0.0286729141
## Lag 3e+05          0.004111172          -0.02101450        -0.0211628960
## Lag 4e+05         -0.008814520          -0.02880465         0.0003342111
## Lag 5e+05         -0.010103967           0.01393777         0.0120490514
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000          1.000000e+00
## Lag 1e+05 -0.002662988         -0.0007490523          3.236644e-02
## Lag 2e+05  0.004546091          0.0133263894          1.462424e-02
## Lag 3e+05 -0.002668186         -0.0051560954          2.777050e-03
## Lag 4e+05  0.015900162         -0.0303057837         -8.123473e-05
## Lag 5e+05 -0.015075875          0.0010262825         -1.480856e-02
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05           0.05566385          0.181118613         -0.032220069
## Lag 2e+05          -0.03116564          0.043493230          0.002258639
## Lag 3e+05          -0.03484217          0.032881937          0.000263964
## Lag 4e+05          -0.01074866          0.001968077          0.006899068
## Lag 5e+05           0.01936004         -0.005254293         -0.023132816
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.3567                1.0125               -0.8170 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.8573               -0.6792                0.4904 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7213272             0.3113035             0.4139214 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3912822             0.4969950             0.6238513 
## Joint P-value (lower = worse):  0.9364104 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4239                0.0314               -1.1925 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1454               -1.7723                0.6594 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.67163659            0.97494739            0.23306955 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.88440555            0.07633805            0.50962689 
## Joint P-value (lower = worse):  0.7362921 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.3578                0.7206                1.0449 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.7166                0.0958               -1.4475 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7204643             0.4711506             0.2960875 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4735965             0.9236777             0.1477468 
## Joint P-value (lower = worse):  0.482485 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.8455                0.3176               -1.4854 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2135               -1.2084               -0.7772 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3978325             0.7507771             0.1374440 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2249442             0.2269111             0.4370459 
## Joint P-value (lower = worse):  0.364932 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.2661                0.1866               -0.2966 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.1905                1.0921                0.4786 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7901500             0.8519839             0.7667849 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2338670             0.2748012             0.6322126 
## Joint P-value (lower = worse):  0.4862998 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.8432               -1.4042                1.3774 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.8573               -0.1859                1.7786 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.06529489            0.16027117            0.16837747 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.39127586            0.85253463            0.07530033 
## Joint P-value (lower = worse):  0.1361442 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -2.1351               -0.3087                0.1256 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1273                0.9859               -1.8784 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.03275344            0.75756562            0.90004783 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.89870580            0.32418776            0.06033228 
## Joint P-value (lower = worse):  0.3907398 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.9140               -0.8390               -0.7464 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.1839               -2.3059                0.9874 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.36073577            0.40149645            0.45543046 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.85407010            0.02111746            0.32343032 
## Joint P-value (lower = worse):  0.1765716 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -2.24497 28.947  0.16713        0.16586
## nodefactor.deg.pers.1  0.28153 17.536  0.10125        0.10213
## nodefactor.deg.pers.2  0.05153 18.848  0.10882        0.10980
## nodefactor.race..wa.B  2.12433 12.477  0.07203        0.07517
## nodefactor.race..wa.H  2.22470 17.784  0.10267        0.10759
## nodematch.race..wa.B  -2.20862  4.911  0.02835        0.03070
## nodematch.race..wa.H  -2.11150  8.740  0.05046        0.06100
## nodematch.race..wa.O   2.21252 26.284  0.15175        0.15046
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%    50%     75%  97.5%
## edges                 -59.50 -21.500 -2.500 16.7500 54.500
## nodefactor.deg.pers.1 -34.00 -12.000  0.000 12.0000 35.000
## nodefactor.deg.pers.2 -37.00 -13.000  0.000 13.0000 37.000
## nodefactor.race..wa.B -22.00  -5.997  2.003 11.0032 27.003
## nodefactor.race..wa.H -32.98  -9.978  2.022 14.0220 37.022
## nodematch.race..wa.B  -11.18  -5.179 -2.179  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.312  3.6876 14.688
## nodematch.race..wa.O  -48.89 -15.890  2.110 20.1103 54.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39335299
## nodefactor.deg.pers.1 0.3933530            1.00000000
## nodefactor.deg.pers.2 0.4270905            0.03918501
## nodefactor.race..wa.B 0.2631131            0.09444063
## nodefactor.race..wa.H 0.3561295            0.13959362
## nodematch.race..wa.B  0.1011940            0.02802289
## nodematch.race..wa.H  0.1555471            0.06478973
## nodematch.race..wa.O  0.8061054            0.32071201
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42709051           0.263113107
## nodefactor.deg.pers.1            0.03918501           0.094440628
## nodefactor.deg.pers.2            1.00000000           0.112233076
## nodefactor.race..wa.B            0.11223308           1.000000000
## nodefactor.race..wa.H            0.13056521          -0.001705605
## nodematch.race..wa.B             0.04108212           0.565636528
## nodematch.race..wa.H             0.05650643           0.005843361
## nodematch.race..wa.O             0.35521654          -0.076127341
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.356129519          0.101193953
## nodefactor.deg.pers.1           0.139593621          0.028022889
## nodefactor.deg.pers.2           0.130565211          0.041082125
## nodefactor.race..wa.B          -0.001705605          0.565636528
## nodefactor.race..wa.H           1.000000000         -0.001890924
## nodematch.race..wa.B           -0.001890924          1.000000000
## nodematch.race..wa.H            0.640739849         -0.001066902
## nodematch.race..wa.O           -0.070866155          0.030719789
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.155547084           0.80610535
## nodefactor.deg.pers.1          0.064789727           0.32071201
## nodefactor.deg.pers.2          0.056506433           0.35521654
## nodefactor.race..wa.B          0.005843361          -0.07612734
## nodefactor.race..wa.H          0.640739849          -0.07086615
## nodematch.race..wa.B          -0.001066902           0.03071979
## nodematch.race..wa.H           1.000000000           0.06733397
## nodematch.race..wa.O           0.067333973           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.018277377           0.028991110           0.003190081
## Lag 2e+05  0.026438055          -0.023529167           0.021572302
## Lag 3e+05 -0.029534118           0.017183717           0.010699079
## Lag 4e+05  0.017638859           0.002439095           0.015823106
## Lag 5e+05 -0.007400578          -0.006368242          -0.008162961
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.041785575           0.055658677          0.055476348
## Lag 2e+05          -0.001498313           0.011302634         -0.008753461
## Lag 3e+05           0.038762776           0.003132148          0.007604190
## Lag 4e+05          -0.007342283           0.011759988          0.019184268
## Lag 5e+05          -0.010082251           0.003783970         -0.017713469
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.199254562          0.008101128
## Lag 2e+05          0.024811737          0.017487419
## Lag 3e+05          0.017063647         -0.016174686
## Lag 4e+05         -0.003389890          0.011855810
## Lag 5e+05         -0.009120107         -0.003660872
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.029567661           0.013355870         -0.0006411415
## Lag 2e+05 -0.021766894           0.004103819          0.0056186757
## Lag 3e+05 -0.008913515           0.009776907          0.0259955518
## Lag 4e+05 -0.016390331          -0.001591314         -0.0225515870
## Lag 5e+05 -0.005923561          -0.040061343         -0.0068173171
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.047483101           0.040277538          0.068175602
## Lag 2e+05           0.008866680           0.008316648          0.005941755
## Lag 3e+05          -0.033882174          -0.015103867         -0.016506273
## Lag 4e+05          -0.007186705           0.004160272          0.006328271
## Lag 5e+05          -0.003018393           0.011951116         -0.012584829
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.186285461          0.019461861
## Lag 2e+05          0.013631188         -0.023550918
## Lag 3e+05         -0.004226715          0.002461332
## Lag 4e+05          0.003400592         -0.003269980
## Lag 5e+05          0.042148891          0.015226264
## Chain 3 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.003715166           0.018392817          0.0006485533
## Lag 2e+05  0.012321491           0.032374189         -0.0129937509
## Lag 3e+05 -0.028330959           0.003448576         -0.0205698494
## Lag 4e+05  0.034412835          -0.016338562          0.0256153599
## Lag 5e+05 -0.003958013          -0.005696274          0.0111844075
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.020134688           0.046582249          0.052756296
## Lag 2e+05          -0.022929492          -0.024422890         -0.014319321
## Lag 3e+05          -0.006335654          -0.002215978          0.010974705
## Lag 4e+05           0.001715782           0.004162756          0.002214300
## Lag 5e+05           0.017009454          -0.040035143          0.003783662
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000
## Lag 1e+05          0.188209354        -0.0106325228
## Lag 2e+05          0.024188101         0.0221375341
## Lag 3e+05         -0.018310695         0.0035866727
## Lag 4e+05         -0.009310719         0.0119614526
## Lag 5e+05         -0.035185469         0.0002307658
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.003348819         -0.0008352219           0.032362076
## Lag 2e+05  0.017095008          0.0212950751           0.036017567
## Lag 3e+05  0.013626256          0.0248384720           0.012841842
## Lag 4e+05  0.016840755         -0.0241983085          -0.007146828
## Lag 5e+05 -0.002184000          0.0089986921          -0.005684060
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.009323487            0.06374721          0.059855354
## Lag 2e+05          -0.003083647            0.02971856         -0.005707967
## Lag 3e+05           0.014362124           -0.01236926          0.035308340
## Lag 4e+05           0.036683420           -0.00413593          0.010107098
## Lag 5e+05          -0.003635447           -0.01548344         -0.010643044
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.195574599         -0.013685253
## Lag 2e+05          0.042548884          0.019816074
## Lag 3e+05         -0.017728709          0.022349134
## Lag 4e+05         -0.008521228          0.009983604
## Lag 5e+05          0.002949536          0.001124482
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.010446072          -0.002826157           0.005408614
## Lag 2e+05 -0.002538888          -0.015960206          -0.020941692
## Lag 3e+05 -0.013833980           0.014149105          -0.015593998
## Lag 4e+05  0.011383457           0.009084746           0.008373701
## Lag 5e+05 -0.008609337           0.011510482          -0.021880759
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.022632180           0.044881831          0.069635201
## Lag 2e+05           0.005181617           0.026615354          0.014556965
## Lag 3e+05           0.007897557          -0.009521328         -0.009280946
## Lag 4e+05          -0.012718004          -0.031144696         -0.001446917
## Lag 5e+05           0.004333361          -0.003576541          0.016237099
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000
## Lag 1e+05         0.1546621958          0.024912367
## Lag 2e+05         0.0106362055         -0.001605056
## Lag 3e+05        -0.0031451364         -0.004797163
## Lag 4e+05        -0.0111190964          0.043440311
## Lag 5e+05        -0.0006078774         -0.031972813
## Chain 6 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.015353993          0.0226592567          -0.015739718
## Lag 2e+05 -0.007378274         -0.0052808074           0.003226649
## Lag 3e+05 -0.017927112          0.0029933093           0.026943214
## Lag 4e+05  0.022627308         -0.0007170325           0.016426287
## Lag 5e+05 -0.004492245         -0.0068562198          -0.003191460
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.028997256           0.030268526          0.050421637
## Lag 2e+05          -0.004272918           0.006619729          0.041386370
## Lag 3e+05           0.006386632           0.002255521         -0.009798439
## Lag 4e+05           0.015439194           0.007748616          0.022572924
## Lag 5e+05           0.006895521           0.020689514          0.001612679
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000
## Lag 1e+05           0.16839302         -0.034505196
## Lag 2e+05           0.02469710          0.029313626
## Lag 3e+05           0.02235703         -0.002206819
## Lag 4e+05           0.02419970          0.016419407
## Lag 5e+05           0.03330881         -0.005427539
## Chain 7 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05 -0.003499738           -0.00390604           0.007548034
## Lag 2e+05 -0.002911506           -0.01589353           0.015700302
## Lag 3e+05 -0.021500833           -0.00250599          -0.007505759
## Lag 4e+05 -0.009045602           -0.01012937           0.015867983
## Lag 5e+05 -0.011466993           -0.01515980          -0.010973133
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05           0.038966988            0.07365700          0.058076122
## Lag 2e+05           0.025094290            0.02195529          0.031656156
## Lag 3e+05           0.004860069           -0.01595463          0.027273709
## Lag 4e+05          -0.009694329           -0.01712136          0.040882497
## Lag 5e+05          -0.001187022           -0.02065514         -0.003371861
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000
## Lag 1e+05          0.201802979          -0.01463718
## Lag 2e+05          0.041246898          -0.01168662
## Lag 3e+05          0.022912624          -0.01607396
## Lag 4e+05         -0.001065059          -0.01097764
## Lag 5e+05          0.013131518          -0.02670544
## Chain 8 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.013995808           0.012185962           0.012836960
## Lag 2e+05  0.008795805           0.027235953           0.012480300
## Lag 3e+05  0.004118789          -0.026235372          -0.002879944
## Lag 4e+05  0.008225470           0.001338352           0.000547836
## Lag 5e+05 -0.008273032           0.011656126           0.030974039
##           nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0              1.0000000000            1.00000000          1.000000000
## Lag 1e+05          0.0003997273            0.05859716          0.058301372
## Lag 2e+05         -0.0053557547            0.01818210          0.012509443
## Lag 3e+05          0.0021546900            0.03160297          0.012361007
## Lag 4e+05         -0.0256387516           -0.02187574          0.005269084
## Lag 5e+05         -0.0391240384            0.01087467         -0.024784988
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000
## Lag 1e+05          0.186674420        -0.0317869055
## Lag 2e+05          0.026904656         0.0200257896
## Lag 3e+05          0.031769288        -0.0008415584
## Lag 4e+05         -0.005676078         0.0217211946
## Lag 5e+05          0.018190777         0.0078347328
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.4435                0.1741               -0.6186 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               -1.1810                0.2378               -1.0584 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##                2.0112               -0.5305 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.14887771            0.86181381            0.53620663 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.23760962            0.81202733            0.28987085 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.04430062            0.59578496 
## Joint P-value (lower = worse):  0.2049696 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -1.20882              -0.04722               0.33818 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##              -0.26059               1.66878              -0.01359 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.44651              -2.15089 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.22673379            0.96234185            0.73523083 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.79440754            0.09516025            0.98915642 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.65522828            0.03148515 
## Joint P-value (lower = worse):  0.3738561 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.08882              -0.06079              -0.20455 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               1.01732              -0.63237               1.35506 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.90809              -0.69164 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.92922709            0.95152952            0.83792448 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.30900306            0.52714665            0.17539912 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.05638009            0.48916498 
## Joint P-value (lower = worse):  0.5207301 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.31368              -0.24611              -0.07002 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               0.19899              -1.45694              -0.52990 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.14837               0.03448 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.7537658             0.8055963             0.9441770 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##             0.8422723             0.1451335             0.5961814 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2508163             0.9724913 
## Joint P-value (lower = worse):  0.9511695 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.0668                0.7505               -1.7944 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               -0.4900               -0.1185               -1.6966 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.3628               -1.0278 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.28606822            0.45296483            0.07274395 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.62415615            0.90568704            0.08977053 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.71677464            0.30402749 
## Joint P-value (lower = worse):  0.4762237 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                2.0724                1.4372                0.3469 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##                1.0671                2.0869                0.5103 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.8852                0.8228 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.03822798            0.15064833            0.72867922 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.28590848            0.03689677            0.60982521 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.37603992            0.41061557 
## Joint P-value (lower = worse):  0.4280388 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.2904                1.2499               -0.3615 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##               -0.1161               -2.0285               -0.5203 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               -2.1074                0.1015 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.77151205            0.21134666            0.71772647 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##            0.90756437            0.04250855            0.60287714 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.03508278            0.91916311 
## Joint P-value (lower = worse):  0.46786 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##              -0.35741              -0.30297               2.99597 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##              -0.72409              -0.89094              -0.46070 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.02361              -0.05032 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.720788622           0.761913303           0.002735716 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodematch.race..wa.B 
##           0.469013441           0.372963212           0.645010769 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##           0.306020539           0.959870372 
## Joint P-value (lower = worse):  0.1407411 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -2.50403 28.869  0.16668        0.16623
## nodefactor.race..wa.B  2.09613 12.574  0.07260        0.07358
## nodefactor.race..wa.H  1.91563 17.645  0.10187        0.10678
## nodefactor.region.EW  -0.08727 16.058  0.09271        0.09408
## nodefactor.region.OW  -0.83170 29.503  0.17034        0.17257
## nodematch.race..wa.B  -2.24972  4.874  0.02814        0.02979
## nodematch.race..wa.H  -2.22103  8.696  0.05021        0.05997
## nodematch.race..wa.O   2.14008 26.326  0.15199        0.15123
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%     75%  97.5%
## edges                 -59.50 -22.500 -2.5000 16.5000 53.500
## nodefactor.race..wa.B -22.00  -5.997  2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98  -9.978  2.0220 14.0220 37.022
## nodefactor.region.EW  -31.56 -10.561  0.4392 10.4392 31.439
## nodefactor.region.OW  -59.13 -21.131 -1.1306 18.8694 56.869
## nodematch.race..wa.B  -11.18  -5.179 -2.1787  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.3124  3.6876 14.688
## nodematch.race..wa.O  -48.89 -15.890  2.1103 20.1103 54.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000           0.263224878
## nodefactor.race..wa.B 0.2632249           1.000000000
## nodefactor.race..wa.H 0.3457791          -0.008445144
## nodefactor.region.EW  0.3557890           0.042379687
## nodefactor.region.OW  0.6273515           0.130262577
## nodematch.race..wa.B  0.1038173           0.567372418
## nodematch.race..wa.H  0.1496551           0.002760818
## nodematch.race..wa.O  0.8077871          -0.077362438
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                           0.345779055          0.355788990
## nodefactor.race..wa.B          -0.008445144          0.042379687
## nodefactor.race..wa.H           1.000000000          0.241355829
## nodefactor.region.EW            0.241355829          1.000000000
## nodefactor.region.OW            0.195676435          0.055284443
## nodematch.race..wa.B            0.010434424          0.003540484
## nodematch.race..wa.H            0.636343509          0.127524509
## nodematch.race..wa.O           -0.074898081          0.250932748
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                           0.62735152          0.103817350
## nodefactor.race..wa.B           0.13026258          0.567372418
## nodefactor.race..wa.H           0.19567644          0.010434424
## nodefactor.region.EW            0.05528444          0.003540484
## nodefactor.region.OW            1.00000000          0.045493419
## nodematch.race..wa.B            0.04549342          1.000000000
## nodematch.race..wa.H            0.08128490          0.009196197
## nodematch.race..wa.O            0.52986632          0.024046730
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.149655144           0.80778713
## nodefactor.race..wa.B          0.002760818          -0.07736244
## nodefactor.race..wa.H          0.636343509          -0.07489808
## nodefactor.region.EW           0.127524509           0.25093275
## nodefactor.region.OW           0.081284899           0.52986632
## nodematch.race..wa.B           0.009196197           0.02404673
## nodematch.race..wa.H           1.000000000           0.06830887
## nodematch.race..wa.O           0.068308874           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000            1.00000000
## Lag 1e+05 -0.0051482255           0.018080087            0.07360187
## Lag 2e+05 -0.0287161820          -0.034061093           -0.01929460
## Lag 3e+05 -0.0118868525           0.008869350           -0.01216225
## Lag 4e+05 -0.0128259935          -0.010082551            0.02434032
## Lag 5e+05  0.0009062889           0.006874745           -0.01017732
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.009676068         -0.003175540         0.0732514350
## Lag 2e+05         -0.041900088         -0.019532722        -0.0078588894
## Lag 3e+05         -0.005552732          0.011745033        -0.0004368906
## Lag 4e+05         -0.017504225          0.024883649         0.0018116174
## Lag 5e+05         -0.003616864         -0.004506722         0.0043842759
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.187966498          0.004321310
## Lag 2e+05          0.005610677         -0.028014534
## Lag 3e+05         -0.012156459          0.008978771
## Lag 4e+05          0.008994011         -0.023535051
## Lag 5e+05         -0.001515203          0.003668919
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.012563194          -0.007854642           0.039111746
## Lag 2e+05 -0.037559057           0.010217969           0.022476928
## Lag 3e+05 -0.006951224          -0.008019785          -0.003467081
## Lag 4e+05  0.006083711           0.001243205          -0.006313127
## Lag 5e+05 -0.023525396           0.016212344           0.018326946
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.016557748          0.034831493          0.074526965
## Lag 2e+05         -0.027818755         -0.007811295         -0.006941076
## Lag 3e+05          0.012648468          0.010124211         -0.011482909
## Lag 4e+05          0.002195528         -0.005511849         -0.014409185
## Lag 5e+05         -0.002628903          0.011789198          0.027954073
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000
## Lag 1e+05         0.1806438377          0.018178618
## Lag 2e+05         0.0357517653         -0.028246994
## Lag 3e+05        -0.0029877950          0.005203405
## Lag 4e+05         0.0003973303          0.017037664
## Lag 5e+05        -0.0059652374         -0.016305115
## Chain 3 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0307702662           0.027658872           0.055937880
## Lag 2e+05 -0.0263087761           0.004076542           0.021847458
## Lag 3e+05  0.0002046685          -0.015185066          -0.008181475
## Lag 4e+05  0.0070076143           0.010151361           0.020801691
## Lag 5e+05  0.0040769354          -0.016319036           0.017820980
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05          0.048668625           0.01863045          0.039231623
## Lag 2e+05         -0.008612631          -0.02989991          0.013679940
## Lag 3e+05         -0.001862586           0.02275201         -0.008703889
## Lag 4e+05          0.014764573           0.00920284          0.010661858
## Lag 5e+05          0.009838090          -0.01007743          0.020744446
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.194433060          0.022242590
## Lag 2e+05          0.042855566         -0.036667996
## Lag 3e+05          0.007954734         -0.012179886
## Lag 4e+05         -0.011968980          0.009819413
## Lag 5e+05         -0.009866694          0.002893492
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.002157829            0.01389553           0.061200101
## Lag 2e+05  0.007970824            0.03390481          -0.008291249
## Lag 3e+05 -0.004324041           -0.02153689          -0.008340931
## Lag 4e+05 -0.020455652            0.03867754           0.021025007
## Lag 5e+05 -0.008411448           -0.01705892          -0.007514773
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.021052922          0.031306261          0.062382057
## Lag 2e+05          0.001132258         -0.013720233          0.011496071
## Lag 3e+05         -0.008612242         -0.006297541          0.024500613
## Lag 4e+05         -0.024377525          0.007895268         -0.016736448
## Lag 5e+05          0.002172048         -0.020585531         -0.009950879
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.164977181          0.021037626
## Lag 2e+05          0.045736136         -0.018603930
## Lag 3e+05          0.003765938          0.020998196
## Lag 4e+05          0.014788824         -0.008541293
## Lag 5e+05          0.023880579          0.012420447
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.001126976          -0.008614565          0.0632114065
## Lag 2e+05  0.004613867           0.001418082          0.0141330420
## Lag 3e+05  0.003549017          -0.009972491          0.0110560963
## Lag 4e+05  0.007962901           0.008042538         -0.0004663387
## Lag 5e+05 -0.016566930           0.040009064          0.0311918301
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.012078496         -0.009092982           0.05481364
## Lag 2e+05          0.033204153         -0.024252546           0.01832972
## Lag 3e+05          0.009638882          0.008936184          -0.01628705
## Lag 4e+05         -0.010695274          0.013137050           0.01222808
## Lag 5e+05         -0.002749617          0.006983583           0.01975620
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.000000e+00
## Lag 1e+05          0.169516126         1.206951e-03
## Lag 2e+05          0.037889850         6.560842e-05
## Lag 3e+05          0.017798825         3.270498e-03
## Lag 4e+05          0.008062528        -9.132545e-03
## Lag 5e+05         -0.027796533        -5.184394e-03
## Chain 6 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000e+00           1.000000000          1.0000000000
## Lag 1e+05 -3.801329e-03          -0.022654120          0.0808603495
## Lag 2e+05  7.905742e-05          -0.006993734          0.0090119787
## Lag 3e+05  3.097944e-02           0.002779985          0.0284070163
## Lag 4e+05 -5.799793e-03          -0.031871457          0.0058081313
## Lag 5e+05  1.052638e-02          -0.003034963         -0.0000899058
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.025594789          0.013024831          0.060759362
## Lag 2e+05          0.007960598         -0.041899243         -0.020078295
## Lag 3e+05          0.016925718          0.024069972          0.009147984
## Lag 4e+05          0.010973474         -0.001723083         -0.029415178
## Lag 5e+05          0.003014535          0.010887558         -0.013560911
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.211935284          0.027545834
## Lag 2e+05          0.065184116         -0.002435683
## Lag 3e+05          0.013747252          0.006714949
## Lag 4e+05          0.016426588         -0.007313488
## Lag 5e+05          0.005216342          0.007058131
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.004866641          -0.001795375            0.07480390
## Lag 2e+05  0.005293501           0.003272682            0.01787659
## Lag 3e+05 -0.002910028           0.023337536            0.01877912
## Lag 4e+05 -0.003667916           0.002467533           -0.02200063
## Lag 5e+05  0.001644688           0.017427330            0.03062236
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.004875493          0.002722852          0.073785970
## Lag 2e+05         -0.015566624         -0.012473483         -0.002374406
## Lag 3e+05         -0.008214824         -0.022981870          0.028128997
## Lag 4e+05          0.033901819          0.004410687          0.005995716
## Lag 5e+05          0.030203734         -0.027217539          0.009394454
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000
## Lag 1e+05         0.1775166002         -0.021924045
## Lag 2e+05         0.0436429948         -0.024193268
## Lag 3e+05        -0.0008622112         -0.006742400
## Lag 4e+05        -0.0050546218          0.002676825
## Lag 5e+05         0.0160058537          0.009065279
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.001298655           0.027479917           0.041296691
## Lag 2e+05  0.022514068          -0.006181535           0.027315907
## Lag 3e+05  0.007479708           0.006476298          -0.006085228
## Lag 4e+05 -0.008678003           0.010079270          -0.007166217
## Lag 5e+05  0.004845089           0.018760252           0.015075027
##           nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.023813065          0.025216608          0.081269415
## Lag 2e+05         -0.006269052         -0.003469458         -0.021967368
## Lag 3e+05         -0.007364342          0.011082267         -0.002205280
## Lag 4e+05         -0.002836984         -0.025522067          0.009684236
## Lag 5e+05          0.015690835          0.005639452         -0.014880101
##           nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000
## Lag 1e+05          0.173644869         -0.008618537
## Lag 2e+05          0.047828855          0.007815577
## Lag 3e+05         -0.021092082         -0.002756049
## Lag 4e+05         -0.007505618         -0.007363422
## Lag 5e+05         -0.009964435          0.005413762
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.3043               -0.6521               -0.4722 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##                1.5510               -0.5527               -2.2323 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.7757               -1.3747 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.19212575            0.51434119            0.63679541 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.12089084            0.58043616            0.02559293 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.43793572            0.16921070 
## Joint P-value (lower = worse):  0.1073029 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.0434                1.0702               -0.8438 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##               -0.1562                1.0659                0.6959 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.0831                0.9099 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2967817             0.2845256             0.3987586 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##             0.8758374             0.2864753             0.4865113 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2787442             0.3628855 
## Joint P-value (lower = worse):  0.8380827 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.48163               0.49096              -2.05319 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##              -0.91006              -0.09070              -0.07256 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -2.39470              -0.37769 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.63006878            0.62345305            0.04005425 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.36278936            0.92772779            0.94215769 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.01663418            0.70566275 
## Joint P-value (lower = worse):  0.5465915 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.7478                0.3612                0.5536 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##                0.3064               -1.6667                0.4119 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.0943               -1.8447 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.08050028            0.71795001            0.57986571 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.75932869            0.09557519            0.68040209 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.27380954            0.06507637 
## Joint P-value (lower = worse):  0.6469245 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.20784              -0.09335              -1.84936 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##               1.16278              -0.42246               0.32443 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.31605               0.64049 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.83535667            0.92562183            0.06440555 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.24491676            0.67269256            0.74561361 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.18815772            0.52185227 
## Joint P-value (lower = worse):  0.6296029 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.34967              -1.16833              -0.55616 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##               0.40577               0.44482              -2.05341 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.05933               1.00939 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.72658621            0.24267491            0.57810244 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.68491118            0.65644873            0.04003329 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.95268844            0.31279000 
## Joint P-value (lower = worse):  0.7497782 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.47853               0.82984              -0.20074 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##              -1.40826               0.31724              -0.69919 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.17383               0.05448 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6322724             0.4066304             0.8409046 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##             0.1590540             0.7510633             0.4844345 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8619956             0.9565547 
## Joint P-value (lower = worse):  0.7663956 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.6493               -2.0128               -0.9680 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##               -1.7042               -0.4002               -2.4934 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.0170               -0.9602 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.09909273            0.04414036            0.33303466 
##  nodefactor.region.EW  nodefactor.region.OW  nodematch.race..wa.B 
##            0.08833763            0.68902389            0.01265355 
##  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.30916898            0.33696412 
## Joint P-value (lower = worse):  0.1858979 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -0.7821 28.964  0.16722        0.16870
## nodefactor.deg.pers.1  0.1774 17.500  0.10103        0.10103
## nodefactor.deg.pers.2  0.4216 18.624  0.10753        0.10478
## nodefactor.race..wa.B  2.8833 12.613  0.07282        0.07417
## nodefactor.race..wa.H  2.0190 17.653  0.10192        0.10925
## nodefactor.region.EW   0.0701 15.915  0.09189        0.09328
## nodefactor.region.OW   0.5392 29.570  0.17072        0.17082
## nodematch.race..wa.B  -2.0920  4.938  0.02851        0.02987
## nodematch.race..wa.H  -2.2595  8.669  0.05005        0.06142
## nodematch.race..wa.O   3.0909 26.405  0.15245        0.15215
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%     75%  97.5%
## edges                 -57.50 -20.500 -0.5000 18.5000 56.500
## nodefactor.deg.pers.1 -34.00 -12.000  0.0000 12.0000 35.000
## nodefactor.deg.pers.2 -36.00 -12.000  0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00  -5.997  3.0032 11.0032 28.003
## nodefactor.race..wa.H -32.00  -9.978  2.0220 14.0220 37.022
## nodefactor.region.EW  -31.56 -10.561  0.4392 10.4392 31.439
## nodefactor.region.OW  -57.13 -19.131 -0.1306 20.8694 58.869
## nodematch.race..wa.B  -11.18  -5.179 -2.1787  0.8213  7.821
## nodematch.race..wa.H  -19.31  -8.312 -2.3124  3.6876 14.688
## nodematch.race..wa.O  -48.89 -14.890  3.1103 21.1103 55.110
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.deg.pers.1
## edges                 1.0000000            0.39769960
## nodefactor.deg.pers.1 0.3976996            1.00000000
## nodefactor.deg.pers.2 0.4216276            0.04530148
## nodefactor.race..wa.B 0.2666542            0.10355680
## nodefactor.race..wa.H 0.3459975            0.13563464
## nodefactor.region.EW  0.3646620            0.14292194
## nodefactor.region.OW  0.6297898            0.24491207
## nodematch.race..wa.B  0.1042821            0.04438147
## nodematch.race..wa.H  0.1527024            0.06208016
## nodematch.race..wa.O  0.8078509            0.32477544
##                       nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges                            0.42162756           0.266654153
## nodefactor.deg.pers.1            0.04530148           0.103556805
## nodefactor.deg.pers.2            1.00000000           0.113910469
## nodefactor.race..wa.B            0.11391047           1.000000000
## nodefactor.race..wa.H            0.12407506          -0.005979985
## nodefactor.region.EW             0.14390008           0.042017498
## nodefactor.region.OW             0.27389656           0.134666468
## nodematch.race..wa.B             0.04238212           0.566763765
## nodematch.race..wa.H             0.05621395           0.004733988
## nodematch.race..wa.O             0.35150461          -0.073658062
##                       nodefactor.race..wa.H nodefactor.region.EW
## edges                          3.459975e-01           0.36466201
## nodefactor.deg.pers.1          1.356346e-01           0.14292194
## nodefactor.deg.pers.2          1.240751e-01           0.14390008
## nodefactor.race..wa.B         -5.979985e-03           0.04201750
## nodefactor.race..wa.H          1.000000e+00           0.25136878
## nodefactor.region.EW           2.513688e-01           1.00000000
## nodefactor.region.OW           1.996581e-01           0.06922304
## nodematch.race..wa.B           1.296239e-05           0.01342154
## nodematch.race..wa.H           6.369781e-01           0.13514032
## nodematch.race..wa.O          -7.702702e-02           0.25875691
##                       nodefactor.region.OW nodematch.race..wa.B
## edges                           0.62978978         1.042821e-01
## nodefactor.deg.pers.1           0.24491207         4.438147e-02
## nodefactor.deg.pers.2           0.27389656         4.238212e-02
## nodefactor.race..wa.B           0.13466647         5.667638e-01
## nodefactor.race..wa.H           0.19965809         1.296239e-05
## nodefactor.region.EW            0.06922304         1.342154e-02
## nodefactor.region.OW            1.00000000         4.570808e-02
## nodematch.race..wa.B            0.04570808         1.000000e+00
## nodematch.race..wa.H            0.08321858        -6.381321e-03
## nodematch.race..wa.O            0.52888189         2.854810e-02
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.152702367           0.80785094
## nodefactor.deg.pers.1          0.062080160           0.32477544
## nodefactor.deg.pers.2          0.056213954           0.35150461
## nodefactor.race..wa.B          0.004733988          -0.07365806
## nodefactor.race..wa.H          0.636978053          -0.07702702
## nodefactor.region.EW           0.135140320           0.25875691
## nodefactor.region.OW           0.083218583           0.52888189
## nodematch.race..wa.B          -0.006381321           0.02854810
## nodematch.race..wa.H           1.000000000           0.06651993
## nodematch.race..wa.O           0.066519926           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.031613433           0.003436771           0.019441663
## Lag 2e+05 -0.013746415           0.005345912           0.007133517
## Lag 3e+05 -0.016546318          -0.031646036           0.021901679
## Lag 4e+05 -0.031639351          -0.027769658          -0.018652234
## Lag 5e+05  0.002456981          -0.007232210           0.014330005
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05          -0.025104982           0.047565004          0.058573711
## Lag 2e+05          -0.007881707          -0.030164270         -0.005288859
## Lag 3e+05           0.006751759           0.002594065          0.007794081
## Lag 4e+05          -0.014652234          -0.016348591         -0.017913325
## Lag 5e+05           0.018856239           0.002435978          0.015947083
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.006048924         0.0575223682          0.200163768
## Lag 2e+05          0.009212617        -0.0115202469          0.034807370
## Lag 3e+05          0.006088956        -0.0130525852          0.003149347
## Lag 4e+05         -0.008155319         0.0003809718         -0.030085302
## Lag 5e+05          0.012771694         0.0202498481          0.011940330
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.044145128
## Lag 2e+05         -0.015380097
## Lag 3e+05         -0.017490316
## Lag 4e+05          0.003478832
## Lag 5e+05          0.014105809
## Chain 2 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.015936644          0.0165044690           0.006559786
## Lag 2e+05 -0.013840681         -0.0002018044           0.003627080
## Lag 3e+05  0.001833094          0.0176939797           0.028108855
## Lag 4e+05  0.040373402         -0.0201200479          -0.030665631
## Lag 5e+05  0.022616940          0.0007472339           0.004966585
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000          1.000000000
## Lag 1e+05          -0.007441850            0.03840781          0.016720258
## Lag 2e+05           0.008517708            0.01626892          0.008542367
## Lag 3e+05           0.011610738           -0.01890772         -0.005001262
## Lag 4e+05           0.006026599           -0.02893857          0.014945164
## Lag 5e+05          -0.019363627           -0.01727607         -0.012097725
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.006959333          0.011294831          0.217096868
## Lag 2e+05          0.024932658          0.009940937          0.040619004
## Lag 3e+05         -0.003985981          0.002896406          0.012630105
## Lag 4e+05         -0.002007182          0.038433159          0.006931709
## Lag 5e+05         -0.021728270         -0.012342561         -0.020886549
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.008250764
## Lag 2e+05         -0.007101318
## Lag 3e+05          0.010549961
## Lag 4e+05          0.026465119
## Lag 5e+05          0.017362599
## Chain 3 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.019794438          -0.019959332          -0.008168591
## Lag 2e+05  0.009659463           0.002705125           0.029300856
## Lag 3e+05 -0.013490379          -0.008174919          -0.006936940
## Lag 4e+05 -0.002536747           0.004527562           0.011324225
## Lag 5e+05  0.002669543           0.018438933          -0.020150801
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000           1.000000000          1.000000000
## Lag 1e+05            0.03019365           0.071521403          0.003058476
## Lag 2e+05            0.02692368           0.037707177         -0.006099224
## Lag 3e+05           -0.02104725          -0.006448694          0.009189922
## Lag 4e+05            0.03446706          -0.006818910          0.013687224
## Lag 5e+05            0.03832081           0.001204360          0.013723439
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.004910225          0.033751527          0.177548124
## Lag 2e+05          0.020579824          0.025672562          0.061952618
## Lag 3e+05         -0.025834391         -0.013622571          0.012235136
## Lag 4e+05         -0.020379349         -0.008224187         -0.013800947
## Lag 5e+05         -0.001368661          0.031796939         -0.009244936
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0150508851
## Lag 2e+05        -0.0009015168
## Lag 3e+05        -0.0078086102
## Lag 4e+05        -0.0225016055
## Lag 5e+05         0.0121260452
## Chain 4 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.000000e+00           1.000000000
## Lag 1e+05  0.024789572          9.774336e-03           0.017185684
## Lag 2e+05 -0.040033508         -1.498816e-02          -0.023275756
## Lag 3e+05  0.014025044          5.151470e-03          -0.009889346
## Lag 4e+05 -0.005559105          8.161177e-05          -0.026888561
## Lag 5e+05  0.015469787          1.076395e-02           0.002865431
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.034561910           0.058966627          0.014235081
## Lag 2e+05          -0.004313803          -0.021996908         -0.004842740
## Lag 3e+05           0.025209197           0.008157381          0.004643023
## Lag 4e+05           0.008771794          -0.015833769          0.001356796
## Lag 5e+05           0.016242889          -0.012489013         -0.010018388
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000         1.0000000000           1.00000000
## Lag 1e+05          0.025913319         0.0605196465           0.18990476
## Lag 2e+05         -0.017508844         0.0006247318           0.04667426
## Lag 3e+05          0.031423322         0.0082089296           0.03419479
## Lag 4e+05         -0.033671693        -0.0038137819           0.01216983
## Lag 5e+05         -0.009439765         0.0146424327          -0.01372970
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.008435523
## Lag 2e+05         -0.040274830
## Lag 3e+05          0.016696965
## Lag 4e+05         -0.002804304
## Lag 5e+05          0.011650563
## Chain 5 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.002708544          -0.008652645          0.0066190268
## Lag 2e+05  0.004219943          -0.003884967         -0.0034821032
## Lag 3e+05 -0.004698070           0.011726916         -0.0458092797
## Lag 4e+05 -0.035979053          -0.027168968         -0.0300695084
## Lag 5e+05 -0.018133352          -0.008584440         -0.0007087911
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000         1.0000000000
## Lag 1e+05           0.010334793            0.07716029         0.0003957808
## Lag 2e+05          -0.005537630            0.02833679         0.0258407181
## Lag 3e+05           0.014139884            0.03808707        -0.0021766016
## Lag 4e+05           0.029548608            0.02394257         0.0252239307
## Lag 5e+05          -0.009143647            0.03313241        -0.0103113404
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000          1.000000000           1.00000000
## Lag 1e+05           0.01747162          0.056253122           0.21473499
## Lag 2e+05           0.01156516          0.026100891           0.03822764
## Lag 3e+05          -0.02227493          0.001314315           0.03532601
## Lag 4e+05          -0.02216020          0.008461128           0.02312065
## Lag 5e+05          -0.02120181         -0.015461878           0.01232091
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.009687080
## Lag 2e+05          0.002672384
## Lag 3e+05         -0.005010374
## Lag 4e+05         -0.017255729
## Lag 5e+05         -0.014243104
## Chain 6 
##                   edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0257383767          -0.006114734           0.014571620
## Lag 2e+05  0.0066660721           0.001148262           0.024593184
## Lag 3e+05  0.0166011373          -0.003719335           0.007122277
## Lag 4e+05 -0.0004137175          -0.027355711           0.005559421
## Lag 5e+05  0.0010630218          -0.017767751           0.033156446
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000           1.000000000          1.000000000
## Lag 1e+05            0.01766730           0.074436384          0.014787594
## Lag 2e+05            0.02293558           0.010256860         -0.008428963
## Lag 3e+05           -0.02595264          -0.006861768         -0.003652313
## Lag 4e+05            0.00191971          -0.017186115         -0.023776685
## Lag 5e+05            0.01164023          -0.006159605         -0.008598749
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.009775661          0.095815095          0.198710643
## Lag 2e+05         -0.016908693          0.006770842          0.018048374
## Lag 3e+05          0.005376860         -0.018422234         -0.002411409
## Lag 4e+05          0.014563799         -0.005197572          0.013440276
## Lag 5e+05         -0.009593660         -0.018037344          0.016161255
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0223747650
## Lag 2e+05         0.0051237668
## Lag 3e+05         0.0051499259
## Lag 4e+05        -0.0005848489
## Lag 5e+05        -0.0055415635
## Chain 7 
##                  edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.009104818          0.0099204665           0.004679962
## Lag 2e+05  0.013931793          0.0310362789          -0.008536338
## Lag 3e+05  0.008861808          0.0125728754          -0.012860709
## Lag 4e+05 -0.016737732          0.0095737222           0.022197048
## Lag 5e+05 -0.019072434          0.0001486868           0.008362994
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05          -0.007843112           0.081248871         0.0113510935
## Lag 2e+05          -0.013461012           0.016983983        -0.0104543074
## Lag 3e+05           0.019285994          -0.002414052        -0.0004951615
## Lag 4e+05          -0.017235239          -0.039919032         0.0135100916
## Lag 5e+05           0.037141666           0.006751605         0.0053512632
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.017315287          0.068006618          0.203438702
## Lag 2e+05          0.020027743         -0.010949073          0.046977873
## Lag 3e+05         -0.013853200         -0.017101612          0.036080456
## Lag 4e+05         -0.010777524         -0.006183436          0.003953253
## Lag 5e+05          0.006784148          0.010211798          0.007295238
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.001827574
## Lag 2e+05         -0.002955092
## Lag 3e+05          0.003997275
## Lag 4e+05         -0.001977305
## Lag 5e+05         -0.011833027
## Chain 8 
##                   edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0      1.0000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.0021879140          0.0024272944          -0.020557623
## Lag 2e+05 -0.0212683883          0.0023466103          -0.035154257
## Lag 3e+05  0.0058293953          0.0104313533           0.007137339
## Lag 4e+05  0.0141486817         -0.0009908951          -0.002479882
## Lag 5e+05 -0.0005044316          0.0074317821          -0.008584502
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.009223159           0.041259420          0.013661865
## Lag 2e+05          -0.018475608           0.009882163         -0.013147975
## Lag 3e+05           0.008384685           0.006643589          0.055859686
## Lag 4e+05          -0.017049828          -0.016323818         -0.008659318
## Lag 5e+05          -0.029569144           0.007373270         -0.012037595
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000          1.000000000           1.00000000
## Lag 1e+05        -0.0037002334          0.060768475           0.17981817
## Lag 2e+05        -0.0084642193          0.013189851           0.03394335
## Lag 3e+05         0.0101001085          0.005698145           0.02461184
## Lag 4e+05         0.0005335216         -0.048423829           0.02936621
## Lag 5e+05         0.0094867600         -0.042841997           0.02407725
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0209108263
## Lag 2e+05         0.0007050679
## Lag 3e+05        -0.0080459362
## Lag 4e+05        -0.0111292118
## Lag 5e+05        -0.0013889017
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.0490                0.2255               -0.5141 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -3.6554                0.8009               -1.2237 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.2544               -1.5960                0.6870 
##  nodematch.race..wa.O 
##               -0.1466 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##           0.294184317           0.821572642           0.607206477 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##           0.000256802           0.423202451           0.221079404 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.799150068           0.110495410           0.492074174 
##  nodematch.race..wa.O 
##           0.883457834 
## Joint P-value (lower = worse):  0.1508821 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.5147               -1.6801               -2.1700 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.7509               -0.1408               -0.1289 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.1394               -0.6721               -1.0409 
##  nodematch.race..wa.O 
##               -1.5321 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.12984888            0.09293621            0.03000797 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.45271679            0.88805115            0.89744002 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.25453641            0.50152855            0.29791939 
##  nodematch.race..wa.O 
##            0.12550757 
## Joint P-value (lower = worse):  0.4563898 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.6518               -0.2847               -1.7650 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.1072               -0.7423                1.0809 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.6307                0.3387               -0.6409 
##  nodematch.race..wa.O 
##               -0.3134 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##            0.51449815            0.77590896            0.07756342 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##            0.91462249            0.45792108            0.27976069 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.52825787            0.73482665            0.52155549 
##  nodematch.race..wa.O 
##            0.75400776 
## Joint P-value (lower = worse):  0.5724516 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.4571               -0.5663               -0.6267 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##                0.6473                1.2415                1.5685 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.5994                0.4101                0.7248 
##  nodematch.race..wa.O 
##               -0.3051 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.6476196             0.5711820             0.5308441 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.5174698             0.2144343             0.1167675 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5489222             0.6817434             0.4685972 
##  nodematch.race..wa.O 
##             0.7602913 
## Joint P-value (lower = worse):  0.7888847 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -1.0537                0.2832               -1.3739 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.5158               -0.4967                0.9409 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.2611               -0.9178               -0.4263 
##  nodematch.race..wa.O 
##               -1.0203 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.2920161             0.7769981             0.1694727 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.6059743             0.6194177             0.3467701 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7940015             0.3587356             0.6699013 
##  nodematch.race..wa.O 
##             0.3075659 
## Joint P-value (lower = worse):  0.6247722 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.1929                0.2821                0.6528 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.4709                0.2098                0.1931 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.4073                0.6463                0.2205 
##  nodematch.race..wa.O 
##                0.5227 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.8470064             0.7778700             0.5139096 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.6377199             0.8338511             0.8468983 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6837844             0.5180803             0.8254459 
##  nodematch.race..wa.O 
##             0.6011979 
## Joint P-value (lower = worse):  0.9915551 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##                0.6826                0.0792               -0.2890 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -1.1720               -0.4569                0.4955 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.1050               -0.5880               -1.1276 
##  nodematch.race..wa.O 
##                1.1259 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.4948286             0.9368717             0.7726136 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.2412157             0.6477693             0.6202775 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2691472             0.5565197             0.2595048 
##  nodematch.race..wa.O 
##             0.2602236 
## Joint P-value (lower = worse):  0.8623852 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##               -0.2179               -1.0364               -0.5330 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##               -0.5510                0.1624                0.8739 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.0282                0.2431                0.5000 
##  nodematch.race..wa.O 
##                0.1317 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.pers.1 nodefactor.deg.pers.2 
##             0.8275083             0.2999990             0.5940589 
## nodefactor.race..wa.B nodefactor.race..wa.H  nodefactor.region.EW 
##             0.5816317             0.8709774             0.3821600 
##  nodefactor.region.OW  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9775012             0.8079504             0.6170404 
##  nodematch.race..wa.O 
##             0.8952017 
## Joint P-value (lower = worse):  0.9503468 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.m.testracemix.unbal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x560c19dc3808>
## 
## Iterations:  150 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                     38.02   81345.40    100       1    
## nodefactor.race..wa.B    -47.33   81345.40    100       1    
## nodefactor.race..wa.H    -46.98   81345.40    100       1    
## nodematch.race..wa.B      48.81   81345.40    100       1    
## nodematch.race..wa.H      48.91   81345.40    100       1    
## nodematch.race..wa.O     -46.70   81345.40    100       1    
## deg2+                      -Inf       0.00      0  <1e-04 ***
## nodematch.role.class.I     -Inf       0.00      0  <1e-04 ***
## nodematch.role.class.R     -Inf       0.00      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 2

summary(est.m.testracemix.unbal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x560c3cd3a1a8>
## 
## Iterations:  130 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   4.039e+01  1.064e+04    100  0.9970    
## nodefactor.deg.pers.1  -3.656e-01  6.279e-02      0  <1e-04 ***
## nodefactor.deg.pers.2  -1.012e-01  5.942e-02      0  0.0886 .  
## nodefactor.race..wa.B  -4.959e+01  1.064e+04    100  0.9963    
## nodefactor.race..wa.H  -4.924e+01  1.064e+04    100  0.9963    
## nodematch.race..wa.B    5.107e+01  1.064e+04    100  0.9962    
## nodematch.race..wa.H    5.117e+01  1.064e+04    100  0.9962    
## nodematch.race..wa.O   -4.896e+01  1.064e+04    100  0.9963    
## deg2+                        -Inf  0.000e+00      0  <1e-04 ***
## nodematch.role.class.I       -Inf  0.000e+00      0  <1e-04 ***
## nodematch.role.class.R       -Inf  0.000e+00      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 3

summary(est.m.testracemix.unbal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodefactor("region", 
##     base = 2) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x560c5a0f0880>
## 
## Iterations:  222 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   5.572e+01  1.025e+05    100 0.99957    
## nodefactor.race..wa.B  -6.478e+01  1.025e+05    100 0.99950    
## nodefactor.race..wa.H  -6.438e+01  1.025e+05    100 0.99950    
## nodefactor.region.EW   -2.173e-01  6.920e-02      0 0.00169 ** 
## nodefactor.region.OW   -3.893e-01  4.482e-02      0 < 1e-04 ***
## nodematch.race..wa.B    6.622e+01  1.025e+05    100 0.99948    
## nodematch.race..wa.H    6.632e+01  1.025e+05    100 0.99948    
## nodematch.race..wa.O   -6.411e+01  1.025e+05    100 0.99950    
## deg2+                        -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.I       -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.R       -Inf  0.000e+00      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Model 4

summary(est.m.testracemix.unbal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x560c7751fd30>
## 
## Iterations:  147 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                   11.57002         NA     NA      NA    
## nodefactor.deg.pers.1   -0.36531    0.06297      0 < 1e-04 ***
## nodefactor.deg.pers.2   -0.10251    0.05990      0 0.08698 .  
## nodefactor.race..wa.B  -20.50899         NA     NA      NA    
## nodefactor.race..wa.H  -20.10764         NA     NA      NA    
## nodefactor.region.EW    -0.21697    0.07003      0 0.00195 ** 
## nodefactor.region.OW    -0.38970    0.04473      0 < 1e-04 ***
## nodematch.race..wa.B    21.94142         NA     NA      NA    
## nodematch.race..wa.H    22.04793         NA     NA      NA    
## nodematch.race..wa.O   -19.83352         NA     NA      NA    
## deg2+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg2+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238

Network diagnostics

Model 1

(dx_main1 <- netdx(est.m.testracemix.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2232.137   -0.004 30.520
## nodefactor.deg.pers.1        NA  567.430       NA 19.074
## nodefactor.deg.pers.2        NA  616.600       NA 20.842
## nodefactor.race..wa.B   207.997  210.423    0.012 11.201
## nodefactor.race..wa.H   534.978  534.352   -0.001 17.909
## nodefactor.region.EW         NA  461.804       NA 14.972
## nodefactor.region.OW         NA 1467.109       NA 28.580
## nodematch.race..wa.B     31.179   29.664   -0.049  5.028
## nodematch.race..wa.H    123.312  119.076   -0.034  9.588
## nodematch.race..wa.O   1638.890 1636.102   -0.002 27.873
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.433   -0.180 119.964
## Pct Edges Diss   0.007    0.007    0.005   0.002
plot(dx_main1, type="formation")

plot(dx_main1, type="duration")

plot(dx_main1, type="dissolution")

Model 2

(dx_main2 <- netdx(est.m.testracemix.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2233.270   -0.003 30.943
## nodefactor.deg.pers.1   474.000  473.993    0.000 16.539
## nodefactor.deg.pers.2   605.000  601.848   -0.005 19.358
## nodefactor.race..wa.B   207.997  212.881    0.023 13.684
## nodefactor.race..wa.H   534.978  532.401   -0.005 18.153
## nodefactor.region.EW         NA  461.188       NA 17.029
## nodefactor.region.OW         NA 1465.580       NA 28.846
## nodematch.race..wa.B     31.179   29.009   -0.070  4.885
## nodematch.race..wa.H    123.312  120.546   -0.022  9.362
## nodematch.race..wa.O   1638.890 1637.544   -0.001 25.893
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.912   -0.177 119.990
## Pct Edges Diss   0.007    0.007    0.000   0.002
plot(dx_main2, type="formation")

plot(dx_main2, type="duration")

plot(dx_main2, type="dissolution")

Model 3

(dx_main3 <- netdx(est.m.testracemix.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2230.253   -0.005 29.130
## nodefactor.deg.pers.1        NA  565.398       NA 16.701
## nodefactor.deg.pers.2        NA  615.811       NA 19.816
## nodefactor.race..wa.B   207.997  211.608    0.017 11.645
## nodefactor.race..wa.H   534.978  533.569   -0.003 18.127
## nodefactor.region.EW    445.561  441.524   -0.009 16.202
## nodefactor.region.OW   1278.131 1272.598   -0.004 33.041
## nodematch.race..wa.B     31.179   28.881   -0.074  4.633
## nodematch.race..wa.H    123.312  117.141   -0.050  8.387
## nodematch.race..wa.O   1638.890 1631.098   -0.005 26.456
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  125.687   -0.179 120.352
## Pct Edges Diss   0.007    0.007    0.003   0.002
plot(dx_main3, type="formation")

plot(dx_main3, type="duration")

plot(dx_main3, type="dissolution")

Model 4

(dx_main4 <- netdx(est.m.testracemix.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2240.500 2234.023   -0.003 26.320
## nodefactor.deg.pers.1   474.000  472.558   -0.003 17.151
## nodefactor.deg.pers.2   605.000  602.140   -0.005 20.640
## nodefactor.race..wa.B   207.997  211.787    0.018 12.851
## nodefactor.race..wa.H   534.978  530.781   -0.008 14.943
## nodefactor.region.EW    445.561  443.445   -0.005 14.515
## nodefactor.region.OW   1278.131 1278.702    0.000 26.010
## nodematch.race..wa.B     31.179   29.126   -0.066  4.812
## nodematch.race..wa.H    123.312  116.716   -0.053  8.514
## nodematch.race..wa.O   1638.890 1637.296   -0.001 25.962
## deg2+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                 Target Sim Mean Pct Diff  Sim SD
## Edge Duration  153.000  126.206   -0.175 120.361
## Pct Edges Diss   0.007    0.007   -0.002   0.002
plot(dx_main4, type="formation")

plot(dx_main4, type="duration")

plot(dx_main4, type="dissolution")